SoroushVahidi/scidocs huggingface dataset:
Epidemiological features of chronic low-back pain
(1999)

Although the literature is filled with information about the prevalence and incidence of back pain in general, there is less information about chronic back pain, partly because of a lack of agreement about definition. Chronic back pain is sometimes defined as back pain that lasts for longer than 7-12 weeks. Others define it as pain that lasts beyond the expected period of healing, and acknowledge that chronic pain may not have well-defined underlying pathological causes. Others classify frequently recurring back pain as chronic pain since it intermittently affects an individual over a long period. Most national insurance and industrial sources of data include only those individuals in whom symptoms result in loss of days at work or other disability. Thus, even less is known about the epidemiology of chronic low-back pain with no associated work disability or compensation. Chronic low-back pain has also become a diagnosis of convenience for many people who are actually disabled for socioeconomic, work-related, or psychological reasons. In fact, some people argue that chronic disability in back pain is primarily related to a psychosocial dysfunction. Because the validity and reliability of some of the existing data are uncertain, caution is needed in an assessment of the information on this type of pain.
Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch
(2016)

Giving robots the ability to classify surface textures requires appropriate sensors and algorithms. Inspired by the biology of human tactile perception, we implement a neurorobotic texture classifier with a recurrent spiking neural network, using a novel semisupervised approach for classifying dynamic stimuli. Input to the network is supplied by accelerometers mounted on a robotic arm. The sensor data are encoded by a heterogeneous population of neurons, modeled to match the spiking activity of mechanoreceptor cells. This activity is convolved by a hidden layer using bandpass filters to extract nonlinear frequency information from the spike trains. The resulting high-dimensional feature representation is then continuously classified using a neurally implemented support vector machine. We demonstrate that our system classifies 18 metal surface textures scanned in two opposite directions at a constant velocity. We also demonstrate that our approach significantly improves upon a baseline model that does not use the described feature extraction. This method can be performed in real-time using neuromorphic hardware, and can be extended to other applications that process dynamic stimuli online.
Determination of differential leakage factors in electrical machines with non-symmetrical full and dead-coil windings
(2017)

In this paper Görges polygons are used in conjunction with masses geometry to find an easy and affordable way to compute the differential leakage factor of non symmetrical full and dead coil winding. By following the traditional way, the use of the Ossanna's infinite series which has to be obviously truncated under the bound of a predetermined accuracy is mandatory. In the presented method no infinite series is instead required. An example is then shown and discussed to demonstrate practically the effectiveness of the proposed method.
RUM Extractor: A Facebook Extractor for Data Analysis
(2015)

Social Network Analysis (SNA) is a field of study that focuses on analyzing user profiles and participations on social network channels in order to model relationships between people and to predict certain behaviors or knowledge. To achieve their goals, researchers, interested in SNA, have to extract content and structure from the numerous social networks available today. Existing tools, which help in this task, often require substantial pre-processing or good programming skills which may not be available for all SNA researchers. This paper describes RUM, a data extraction tool which allows researchers to easily extract several types of content and structure that are available on Facebook pages. Consequently, the extracted data can be saved and analyzed. RUM Extractor is easy to set up and use, and it gives flexible options to users to specify the type and amount of content and structure they want to retrieve. The paper also demonstrates how RUM can be exploited by collecting and further analyzing data collected from two popular Arabic news pages.
Decentralized frequency control of a DDG-PV Microgrid in islanded mode
(2015)

This paper presents an innovative approach to control the frequency in Diesel-Driven Generator (DDG)-Photovoltaic (PV) Microgrid (MG) in islanded mode. The common approach is based on hierarchical control: primary control and secondary control. The conventional primary controller is a P-controller. Compared to this, we propose a novel primary controller for PV to mimic the dynamic behavior of the conventional synchronous generator (SG) so that more inertia in the MG can be obtained. The proposed secondary control is accomplished decentralized, too, so that no communication links for the real-time control are required and the single point of failure can be avoided. Although in this approach every controller works locally, the controllers and their parameters are designed centrally. This means, the controllers can be appropriately adjusted, when the parameters or the topology of the system change. The parameters of the controllers are optimized by minimizing the H-inf-norm using a known non-smooth method.
TinyECC: A Configurable Library for Elliptic Curve Cryptography in Wireless Sensor Networks
(2008)

Public Key Cryptography (PKC) has been the enabling technology underlying many security services and protocols in traditional networks such as the Internet. In the context of wireless sensor networks, elliptic curve cryptography (ECC), one of the most efficient types of PKC, is being investigated to provide PKC supportin sensor network applications so that the existing PKC-based solutions can be exploited. This paper presents the design, implementation, and evaluation of TinyECC, a configurable library for ECC operations in wireless sensor networks. The primary objective of TinyECC is to provide a ready-to-use, publicly available software package for ECC-based PKC operations that can be flexibly configured and integrated into sensor network applications. TinyECC provides a number of optimization switches, which can turn specific optimizations on or off based on developers' needs. Different combinations of the optimizations have different execution time andresource consumptions, giving developers great flexibility in integrating TinyECC into sensor network applications. This paperalso reports the experimental evaluation of TinyECC on several common sensor platforms, including MICAz, Tmote Sky, and Imote2. The evaluation results show the impacts of individual optimizations on the execution time and resource consumptions, and give the most computationally efficient and the most storage efficient configuration of TinyECC.
Beyond the 'digital natives' debate: Towards a more nuanced understanding of students' technology experiences
(2010)

The idea of the ‘digital natives’, a generation of tech-savvy young people immersed in digital technologies for which current education systems cannot cater, has gained widespread popularity on the basis of claims rather than evidence. Recent research has shown flaws in the argument that there is an identifiable generation or even a single type of highly adept technology user. For educators, the diversity revealed by these studies provides valuable insights into students’ experiences of technology inside and outside formal education. While this body of work provides a preliminary understanding, it also highlights subtleties and complexities that require further investigation. It suggests, for example, that we must go beyond simple dichotomies evident in the digital natives debate to develop a more sophisticated understanding of our students’ experiences of technology. Using a review of recent research findings as a starting point, this paper identifies some key issues for educational researchers, offers new ways of conceptualizing key ideas using theoretical constructs from Castells, Bourdieu and Bernstein, and makes a case for how we need to develop the debate in order to advance our understanding.
A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns
(2015)

Framing is a sophisticated form of discourse in which the speaker tries to induce a cognitive bias through consistent linkage between a topic and a specific context (frame). We build on political science and communication theory and use probabilistic topic models combined with time series regression analysis (autoregressive distributed-lag models) to gain insights about the language dynamics in the political processes. Processing four years of public statements issued by members of the U.S. Congress, our results provide a glimpse into the complex dynamic processes of framing, attention shifts and agenda setting, commonly known as ‘spin’. We further provide new evidence for the divergence in party discipline in U.S. politics.
Deterministic compressed-sensing matrices: Where Toeplitz meets Golay
(2011)

Recently, the statistical restricted isometry property (STRIP) has been formulated to analyze the performance of deterministic sampling matrices for compressed sensing. In this paper, a class of deterministic matrices which satisfy STRIP with overwhelming probability are proposed, by taking advantage of concentration inequalities using Stein's method. These matrices, called orthogonal symmetric Toeplitz matrices (OSTM), guarantee successful recovery of all but an exponentially small fraction of K-sparse signals. Such matrices are deterministic, Toeplitz, and easy to generate. We derive the STRIP performance bound by exploiting the specific properties of OSTM, and obtain the near-optimal bound by setting the underlying sign sequence of OSTM as the Golay sequence. Simulation results show that these deterministic sensing matrices can offer reconstruction performance similar to that of random matrices.
Perceptual Control Theory 1 Perceptual Control Theory A Model for Understanding the Mechanisms and Phenomena of Control
(2011)

Perceptual Control Theory (PCT) provides a general theory of functioning for organisms. At the conceptual core of the theory is the observation that living things control the perceived environment by means of their behavior. Consequently, the phenomenon of control takes center stage in PCT, with observable behavior playing an important but supporting role. The first part of the paper explains how the PCT model works. This explanation includes a definition of “control” as well as the basic equations from which one can see what is required for control to be possible. The second part of the paper describes demonstrations that the reader can download from the Internet and run, so as to learn the basics of control by experiencing and verifying the phenomenon directly. The third part of the paper shows examples of the application of PCT to different areas of psychological research including learning, developmental psychology, social psychology, and psychotherapy. This summary of the current state of the field celebrates the 50th Anniversary of the first major publication in PCT (Powers, Clark & MacFarland, 1960).
Intelligent Wireless Patient Monitoring and Tracking System (Using Sensor Network and Wireless Communication).
(2014)

* Corresponding author: Ch.Sandeep Kumar Subudhi Abstract Aim of our work is to monitor the human body temperature, blood pressure (BP), Pulse Rate and ECG and tracking the patient location. The human body temperature, BP, Pulse Rate and ECG are detected in the working environment; this can be sensed by using respective sensors. The sensed information is send to the PIC16F877 microcontroller through signal conditioning circuit in the patient unit. A desired amount of sensor value is set and if it is exceeded preliminary steps should be taken by the indicating by buzzer.The sensor information will be transmitted from the patient unit to the main controller unit with the help of Zigbee communication system which is connected with the microcontrollers in the both units. The main controller unit will send those sensed data as well as the location of that patient by the help of GPS Module to the observer/doctor. The observer/doctor can receive the SMS sent by GSM module and further decision can be taken. The message is sent to a mobile phone using Global system mobile (GSM) Modem. MAX232 was a driver between microcontroller and modem.
On speaker adaptation of long short-term memory recurrent neural networks
(2015)

Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture specializing in modeling long-range temporal dynamics. On acoustic modeling tasks, LSTM-RNNs have shown better performance than DNNs and conventional RNNs. In this paper, we conduct an extensive study on speaker adaptation of LSTM-RNNs. Speaker adaptation helps to reduce the mismatch between acoustic models and testing speakers. We have two main goals for this study. First, on a benchmark dataset, the existing DNN adaptation techniques are evaluated on the adaptation of LSTM-RNNs. We observe that LSTMRNNs can be effectively adapted by using speaker-adaptive (SA) front-end, or by inserting speaker-dependent (SD) layers. Second, we propose two adaptation approaches that implement the SD-layer-insertion idea specifically for LSTM-RNNs. Using these approaches, speaker adaptation improves word error rates by 3-4% relative over a strong LSTM-RNN baseline. This improvement is enlarged to 6-7% if we exploit SA features for further adaptation.
KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition
(2013)

We propose a novel regularized adaptation technique for context dependent deep neural network hidden Markov models (CD-DNN-HMMs). The CD-DNN-HMM has a large output layer and many large hidden layers, each with thousands of neurons. The huge number of parameters in the CD-DNN-HMM makes adaptation a challenging task, esp. when the adaptation set is small. The technique developed in this paper adapts the model conservatively by forcing the senone distribution estimated from the adapted model to be close to that from the unadapted model. This constraint is realized by adding Kullback-Leibler divergence (KLD) regularization to the adaptation criterion. We show that applying this regularization is equivalent to changing the target distribution in the conventional backpropagation algorithm. Experiments on Xbox voice search, short message dictation, and Switchboard and lecture speech transcription tasks demonstrate that the proposed adaptation technique can provide 2%-30% relative error reduction against the already very strong speaker independent CD-DNN-HMM systems using different adaptation sets under both supervised and unsupervised adaptation setups.
Multi-Graph Learning with Positive and Unlabeled Bags
(2014)

In this paper, we formulate a new multi-graph learning task with only positive and unlabeled bags, where labels are only available for bags but not for individual graphs inside the bag. This problem setting raises significant challenges because bag-of-graph setting does not have features to directly represent graph data, and no negative bags exits for deriving discriminative classification models. To solve the challenge, we propose a puMGL learning framework which relies on two iteratively combined processes for multigraph learning: (1) deriving features to represent graphs for learning; and (2) deriving discriminative models with only positive and unlabeled graph bags. For the former, we derive a subgraph scoring criterion to select a set of informative subgraphs to convert each graph into a feature space. To handle unlabeled bags, we assign a weight value to each bag and use the adjusted weight values to select most promising unlabeled bags as negative bags. A margin graph pool (MGP), which contains some representative graphs from positive bags and identified negative bags, is used for selecting subgraphs and training graph classifiers. The iterative subgraph scoring, bag weight updating, and MGP based graph classification forms a closed loop to find optimal subgraphs and most suitable unlabeled bags for multi-graph learning. Experiments and comparisons on real-world multigraph data demonstrate the algorithm performance.
Superimposed Sparse Parameter Classifiers for Face Recognition
(2017)

In this paper, a novel classifier, called superimposed sparse parameter (SSP) classifier is proposed for face recognition. SSP is motivated by two phase test sample sparse representation (TPTSSR) and linear regression classification (LRC), which can be treated as the extended of sparse representation classification (SRC). SRC uses all the train samples to produce the sparse representation vector for classification. The LRC, which can be interpreted as L2-norm sparse representation, uses the distances between the test sample and the class subspaces for classification. TPTSSR is also L2-norm sparse representation and uses two phase to compute the distance for classification. Instead of the distances, the SSP classifier employs the SSPs, which can be expressed as the sum of the linear regression parameters of each class in iterations, is used for face classification. Further, the fast SSP (FSSP) classifier is also suggested to reduce the computation cost. A mass of experiments on Georgia Tech face database, ORL face database, CVL face database, AR face database, and CASIA face database are used to evaluate the proposed algorithms. The experimental results demonstrate that the proposed methods achieve better recognition rate than the LRC, SRC, collaborative representation-based classification, regularized robust coding, relaxed collaborative representation, support vector machine, and TPTSSR for face recognition under various conditions.
Face Recognition Machine Vision System Using Eigenfaces
(2017)

Face Recognition is a common problem in Machine Learning. This technology has already been widely used in our lives. For example, Facebook can automatically tag people’s faces in images, and also some mobile devices use face recognition to protect private security. Face images comes with different background, variant illumination, different facial expression and occlusion. There are a large number of approaches for the face recognition. Different approaches for face recognition have been experimented with specific databases which consist of single type, format and composition of image. Doing so, these approaches don’t suit with different face databases. One of the basic face recognition techniques is eigenface which is quite simple, efficient, and yields generally good results in controlled circumstances. So, this paper presents an experimental performance comparison of face recognition using Principal Component Analysis (PCA) and Normalized Principal Component Analysis (NPCA). The experiments are carried out on the ORL (ATT) and Indian face database (IFD) which contain variability in expression, pose, and facial details. The results obtained for the two methods have been compared by varying the number of training images. MATLAB is used for implementing algorithms also.
Analog Imagery in Mental Model Reasoning: Depictive Models
(1996)

We investigated whether people can use analog imagery to model the behavior of a simple mechanical interaction. Subjects saw a static computer display of two touching gears that had different diameters. Their task was to determine whether marks on each gear would meet if the gears rotated inward. This task added a problem of coordination to the typical analog rotation task in that the gears had a physical interdependency; the angular velocity of one gear depended on the angular velocity of the other gear. In the first experiment, we found the linear relationship between response time and angular disparity that indicates analog imagery. In the second experiment, we found that people can also solve the problem through a non-analog, visual comparison. We also found that people of varying spatial ability could switch between analog and non-analog solutions if instructed to do so. In the third experiment, we examined whether the elicitation of physical knowledge would influence solution strategies. To do so, we manipulated the visual realism of the gear display. Subjects who saw the most realistic gears coordinated their transformations by using the surfaces of the gears, as though they were relying on the friction connecting the surfaces. Subjects who saw more schematic displays relied on analytic strategies, such as comparing the ratios made by the angles and/or diameters of the two gears. To explain the relationship between spatial and physical knowledge found in the experiments, we constructed a computer simulation of what we call depictive modeling. In a depictive model, general spatial knowledge and context-sensitive physical knowledge have the same ontology. This is different from prior simulations in which a non-analog representation would be needed to coordinate the analog behaviors of physical objects. In our simulation, the inference that coordinates the gear motions emerges from the analog rotations themselves. We suggest that mental depictions create a bridge between imagery and mental model research by positing the referent as the primary conceptual entity.
Mining Public Transport User Behaviour from Smart Card Data
(2006)

In urban public transport, smart card data is made of millions of observations of users boarding vehicles over the network across several days. The issue addresses whether data mining techniques can be used to study user behaviour from these observations. This must be done with the help of transportation planning knowledge. Hence, this paper presents a common “transportation planning/data mining” methodology for user behaviour analysis. Experiments were conducted on data from a Canadian transit authority. This experience demonstrates that a combination of planning knowledge and data mining tool allows producing travel behaviours indicators, mainly regarding regularity and daily patterns, from data issued from operational and management system. Results show that the public transport users of this study can rapidly be divided in four major behavioural groups, whatever type of ticket they use. Copyright © 2006 IFAC.
stagNet: An Attentive Semantic RNN for Group Activity Recognition
(2018)

Group activity recognition plays a fundamental role in a variety of applications, e.g. sports video analysis and intelligent surveillance. How to model the spatio-temporal contextual information in a scene still remains a crucial yet challenging issue. We propose a novel attentive semantic recurrent neural network (RNN), dubbed as stagNet, for understanding group activities in videos, based on the spatio-temporal attention and semantic graph. A semantic graph is explicitly modeled to describe the spatial context of the whole scene, which is further integrated with the temporal factor via structural-RNN. Benefiting from the ‘factor sharing’ and ‘message passing’ mechanisms, our model is capable of extracting discriminative spatio-temporal features and capturing inter-group relationships. Moreover, we adopt a spatio-temporal attention model to attend to key persons/frames for improved performance. Two widely-used datasets are employed for performance evaluation, and the extensive results demonstrate the superiority of our method.